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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

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dc.citedby8
dc.contributor.authorQayyum A.en_US
dc.contributor.authorSaeed Malik A.en_US
dc.contributor.authorSaad N.M.en_US
dc.contributor.authorIqbal M.en_US
dc.contributor.authorAbdullah M.F.en_US
dc.contributor.authorRasheed W.en_US
dc.contributor.authorAbdullah T.A.B.R.en_US
dc.contributor.authorBin Jafaar M.Y.en_US
dc.contributor.authorid57211138712en_US
dc.contributor.authorid12800348400en_US
dc.contributor.authorid56567441400en_US
dc.contributor.authorid54386959400en_US
dc.contributor.authorid57188825497en_US
dc.contributor.authorid24475459400en_US
dc.contributor.authorid56594684600en_US
dc.contributor.authorid57193519737en_US
dc.date.accessioned2023-05-29T07:24:28Z
dc.date.available2023-05-29T07:24:28Z
dc.date.issued2019
dc.descriptionAerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unmanned aerial vehicles (UAV); Discrete tchebichef transforms; Discriminative features; Finite Ridgelet Transform; Histogram of oriented gradients; Image processing and computer vision; Scale invariant feature transforms; SIFT; Sparse coding; Classification (of information)en_US
dc.description.abstractThis work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition. � 2017, The Natural Computing Applications Forum.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00521-017-3300-5
dc.identifier.epage3607
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85039749366
dc.identifier.spage3587
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85039749366&doi=10.1007%2fs00521-017-3300-5&partnerID=40&md5=6fe11b9fa23c604501e4d591eedc41b5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24550
dc.identifier.volume31
dc.publisherSpringer Londonen_US
dc.sourceScopus
dc.sourcetitleNeural Computing and Applications
dc.titleImage classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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